Genome Analysis Toolkit

Variant Discovery in High-Throughput Sequencing Data

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Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery
and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size.
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BQSRPipelineSpark specific arguments

This table summarizes the command-line arguments that are specific to this tool. For more details on each argument, see the list further down below the table or click on an argument name to jump directly to that entry in the list.

maximum number of bytes to read from a file into each partition of reads. Setting this higher will result in fewer partitions. Note that this will not be equal to the size of the partition in memory. Defaults to 0, which uses the default split size (determined by the Hadoop input format, typically the size of one HDFS block).

Indices to use for the read inputs. If specified, an index must be provided for every read input and in the same order as the read inputs. If this argument is not specified, the path to the index for each input will be inferred automatically.

Validation stringency for all SAM/BAM/CRAM/SRA files read by this program. The default stringency value SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.

maximum number of bytes to read from a file into each partition of reads. Setting this higher will result in fewer partitions. Note that this will not be equal to the size of the partition in memory. Defaults to 0, which uses the default split size (determined by the Hadoop input format, typically the size of one HDFS block).

Assign a default base quality
If reads are missing some or all base quality scores, this value will be used for all base quality scores.
By default this is set to -1 to disable default base quality assignment.

default quality for the base deletions covariate
A default base qualities to use as a prior (reported quality) in the mismatch covariate model. This value will replace all base qualities in the read for this default value. Negative value turns it off. [default is on]

Emit original base qualities under the OQ tag
The tool is capable of writing out the original quality scores of each read in the recalibrated output file
under the "OQ" tag. By default, this behavior is disabled because emitting original qualities results in a
significant increase of the file size. Use this flag to turn on emission of original qualities.

One or more genomic intervals to exclude from processing
Use this argument to exclude certain parts of the genome from the analysis (like -L, but the opposite).
This argument can be specified multiple times. You can use samtools-style intervals either explicitly on the
command line (e.g. -XL 1 or -XL 1:100-200) or by loading in a file containing a list of intervals
(e.g. -XL myFile.intervals).

Global Qscore Bayesian prior to use for BQSR
If specified, the value of this argument will be used as a flat prior for all mismatching quality scores instead
of the reported quality score (assigned by the sequencer).

Size of the k-mer context to be used for base insertions and deletions
The context covariate will use a context of this size to calculate its covariate value for base insertions and deletions. Must be between 1 and 13 (inclusive). Note that higher values will increase runtime and required java heap size.

default quality for the base insertions covariate
A default base qualities to use as a prior (reported quality) in the insertion covariate model. This parameter is used for all reads without insertion quality scores for each base. [default is on]

Amount of padding (in bp) to add to each interval you are excluding.
Use this to add padding to the intervals specified using -XL. For example, '-XL 1:100' with a
padding value of 20 would turn into '-XL 1:80-120'. This is typically used to add padding around targets when
analyzing exomes.

Interval merging rule for abutting intervals
By default, the program merges abutting intervals (i.e. intervals that are directly side-by-side but do not
actually overlap) into a single continuous interval. However you can change this behavior if you want them to be
treated as separate intervals instead.

The --interval-merging-rule argument is an enumerated type (IntervalMergingRule), which can have one of the following values:

Amount of padding (in bp) to add to each interval you are including.
Use this to add padding to the intervals specified using -L. For example, '-L 1:100' with a
padding value of 20 would turn into '-L 1:80-120'. This is typically used to add padding around targets when
analyzing exomes.

Set merging approach to use for combining interval inputs
By default, the program will take the UNION of all intervals specified using -L and/or -XL. However, you can
change this setting for -L, for example if you want to take the INTERSECTION of the sets instead. E.g. to
perform the analysis only on chromosome 1 exomes, you could specify -L exomes.intervals -L 1 --interval-set-rule
INTERSECTION. However, it is not possible to modify the merging approach for intervals passed using -XL (they will
always be merged using UNION).
Note that if you specify both -L and -XL, the -XL interval set will be subtracted from the -L interval set.

The --interval-set-rule argument is an enumerated type (IntervalSetRule), which can have one of the following values:

UNION

Take the union of all intervals

INTERSECTION

Take the intersection of intervals (the subset that overlaps all intervals specified)

minimum quality for the bases in the tail of the reads to be considered
Reads with low quality bases on either tail (beginning or end) will not be considered in the context. This parameter defines the quality below which (inclusive) a tail is considered low quality

The maximum cycle value permitted for the Cycle covariate
The cycle covariate will generate an error if it encounters a cycle greater than this value.
This argument is ignored if the Cycle covariate is not used.

Size of the k-mer context to be used for base mismatches
The context covariate will use a context of this size to calculate its covariate value for base mismatches. Must be between 1 and 13 (inclusive). Note that higher values will increase runtime and required java heap size.

default quality for the base mismatches covariate
A default base qualities to use as a prior (reported quality) in the mismatch covariate model. This value will replace all base qualities in the read for this default value. Negative value turns it off. [default is off]

Don't recalibrate bases with quality scores less than this threshold (with -bqsr)
This flag tells GATK not to modify quality scores less than this value. Instead they will be written out unmodified in the recalibrated BAM file.
In general it's unsafe to change qualities scores below < 6, since base callers use these values to indicate random or bad bases.
For example, Illumina writes Q2 bases when the machine has really gone wrong. This would be fine in and of itself,
but when you select a subset of these reads based on their ability to align to the reference and their dinucleotide effect,
your Q2 bin can be elevated to Q8 or Q10, leading to issues downstream.

Quantize quality scores to a given number of levels
Turns on the base quantization module. It requires a recalibration report.
A value of 0 here means "do not quantize".
Any value greater than zero will be used to recalculate the quantization using that many levels.
Negative values mean that we should quantize using the recalibration report's quantization level.

Exclusion: This argument cannot be used at the same time as static-quantized-quals, round-down-quantized.

number of distinct quality scores in the quantized output
BQSR generates a quantization table for quick quantization later by subsequent tools. BQSR does not quantize the base qualities, this is done by the engine with the -qq or -bqsr options.
This parameter tells BQSR the number of levels of quantization to use to build the quantization table.

Indices to use for the read inputs. If specified, an index must be provided for every read input and in the same order as the read inputs. If this argument is not specified, the path to the index for each input will be inferred automatically.

Validation stringency for all SAM/BAM/CRAM/SRA files read by this program. The default stringency value SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.

The --read-validation-stringency argument is an enumerated type (ValidationStringency), which can have one of the following values:

Round quals down to nearest quantized qual
Round down quantized only works with the static_quantized_quals option, and should not be used with
the dynamic binning option provided by quantize_quals. When roundDown = false, rounding is done in
probability space to the nearest bin. When roundDown = true, the value is rounded to the nearest bin
that is smaller than the current bin.

Exclusion: This argument cannot be used at the same time as quantize-quals.

Use static quantized quality scores to a given number of levels (with -bqsr)
Static quantized quals are entirely separate from the quantize_qual option which uses dynamic binning.
The two types of binning should not be used together.

Exclusion: This argument cannot be used at the same time as quantize-quals.

Use the base quality scores from the OQ tag
This flag tells GATK to use the original base qualities (that were in the data before BQSR/recalibration) which
are stored in the OQ tag, if they are present, rather than use the post-recalibration quality scores. If no OQ
tag is present for a read, the standard qual score will be used.